FPGA-based soft-core processors for image processing applications

With security and surveillance, there is an increasing need to process image data efficiently and effectively either at source or in a large data network. Whilst a Field-Programmable Gate Array (FPGA) has been seen as a key technology for enabling this, the design process has been viewed as problematic in terms of the time and effort needed for implementation and verification. The work here proposes a different approach of using optimized FPGA-based soft-core processors which allows the user to exploit the task and data level parallelism to achieve the quality of dedicated FPGA implementations whilst reducing design time. The paper also reports some preliminary
progress on the design flow to program the structure. An implementation for a Histogram of Gradients algorithm is also reported which shows that a performance of 328 fps can be achieved with this design approach, whilst avoiding the long design time, verification and debugging steps associated with conventional FPGA implementations.

Posterior Probability Modeling and Image Classification for Archaeological Site Prospection: Building a Survey Efficacy Model for Identifying Neolithic Felsite Workshops in the Shetland Islands

The application of custom classification techniques and posterior probability modeling (PPM) using Worldview-2 multispectral imagery to archaeological field survey is presented in this paper. Research is focused on the identification of Neolithic felsite stone tool workshops in the North Mavine region of the Shetland Islands in Northern Scotland. Sample data from known workshops surveyed using differential GPS are used alongside known non-sites to train a linear discriminant analysis (LDA) classifier based on a combination of datasets including Worldview-2 bands, band difference ratios (BDR) and topographical derivatives. Principal components analysis is further used to test and reduce dimensionality caused by redundant datasets. Probability models were generated by LDA using principal components and tested with sites identified through geological field survey. Testing shows the prospective ability of this technique and significance between 0.05 and 0.01, and gain statistics between 0.90 and 0.94, higher than those obtained using maximum likelihood and random forest classifiers. Results suggest that this approach is best suited to relatively homogenous site types, and performs better with correlated data sources. Finally, by combining posterior probability models and least-cost analysis, a survey least-cost efficacy model is generated showing the utility of such approaches to archaeological field survey.

Design and Implementation of a Low Cost Virtual Rugby Decision Making Interactive

The paper describes the design and implementation of a novel low cost virtual rugby decision making interactive for use in a visitor centre. Original laboratory-based experimental work in decision making in rugby, using a virtual reality headset [1] is adapted for use in a public visitor centre, with consideration given to usability, costs, practicality and health and safety. Movement of professional rugby players was captured and animated within a virtually recreated stadium. Users then interact with these virtual representations via use of a lowcost sensor (Microsoft Kinect) to attempt to block them. Retaining the principles of perception and action, egocentric viewpoint, immersion, sense of presence, representative design and game design the system delivers an engaging and effective interactive to illustrate the underlying scientific principles of deceptive movement. User testing highlighted the need for usability, system robustness, fair and accurate scoring, appropriate level of difficulty and enjoyment.

Early warning systems for cyber defence

Cybercriminals ramp up their efforts with sophisticated techniques while defenders gradually update their typical security measures. Attackers often have a long-term interest in their targets. Due to a number of factors such as scale, architecture and nonproductive traffic however it makes difficult to detect them using typical intrusion detection techniques. Cyber early warning systems (CEWS) aim at alerting such attempts in their nascent stages using preliminary indicators. Design and implementation of such systems involves numerous research challenges such as generic set of indicators, intelligence gathering, uncertainty reasoning and information fusion. This paper discusses such challenges and presents the reader with compelling motivation. A carefully deployed empirical analysis using a real world attack scenario and a real network traffic capture is also presented.

Abnormal Pedestrian Trajectory analysis based on arbitrary-length clustering

This papers examines the use of trajectory distance measures and clustering techniques to define normal
and abnormal trajectories in the context of pedestrian tracking in public spaces. In order to detect abnormal
trajectories, what is meant by a normal trajectory in a given scene is firstly defined. Then every trajectory
that deviates from this normality is classified as abnormal. By combining Dynamic Time Warping and a
modified K-Means algorithms for arbitrary-length data series, we have developed an algorithm for trajectory
clustering and abnormality detection. The final system performs with an overall accuracy of 83% and 75%
when tested in two different standard datasets.

Field investigation of contactless displacement measurement using computer vision systems for civil engineering applications

Much of the bridge stock on major transport links in North America and Europe was constructed in the 1950s and 1960s and has since deteriorated or is carrying loads far in excess of the original design loads. Structural Health Monitoring Systems (SHM) can provide valuable information on the bridge capacity but the application of such systems is currently limited by access and bridge type. This paper investigates the use of computer vision systems for SHM. A series of field tests have been carried out to test the accuracy of displacement measurements using contactless methods. A video image of each test was processed using a modified version of the optical flow tracking method to track displacement. These results have been validated with an established measurement method using linear variable differential transformers (LVDTs). The results obtained from the algorithm provided an accurate comparison with the validation measurements. The calculated displacements agree within 2% of the verified LVDT measurements, a number of post processing methods were then applied to attempt to reduce this error.

Machine learning classification of surgical pathology reports and chunk recognition for information extraction noise reduction

Background and aims: Machine learning techniques for the text mining of cancer-related clinical documents have not been sufficiently explored. Here some techniques are presented for the pre-processing of free-text breast cancer pathology reports, with the aim of facilitating the extraction of information relevant to cancer staging.

Materials and methods: The first technique was implemented using the freely available software RapidMiner to classify the reports according to their general layout: ‘semi-structured’ and ‘unstructured’. The second technique was developed using the open source language engineering framework GATE and aimed at the prediction of chunks of the report text containing information pertaining to the cancer morphology, the tumour size, its hormone receptor status and the number of positive nodes. The classifiers were trained and tested respectively on sets of 635 and 163 manually classified or annotated reports, from the Northern Ireland Cancer Registry.

Results: The best result of 99.4% accuracy – which included only one semi-structured report predicted as unstructured – was produced by the layout classifier with the k nearest algorithm, using the binary term occurrence word vector type with stopword filter and pruning. For chunk recognition, the best results were found using the PAUM algorithm with the same parameters for all cases, except for the prediction of chunks containing cancer morphology. For semi-structured reports the performance ranged from 0.97 to 0.94 and from 0.92 to 0.83 in precision and recall, while for unstructured reports performance ranged from 0.91 to 0.64 and from 0.68 to 0.41 in precision and recall. Poor results were found when the classifier was trained on semi-structured reports but tested on unstructured.

Conclusions: These results show that it is possible and beneficial to predict the layout of reports and that the accuracy of prediction of which segments of a report may contain certain information is sensitive to the report layout and the type of information sought.

Deep Corals, Deep Learning: Moving the Deep Net Towards Real-Time Image Annotation

The mismatch between human capacity and the acquisition of Big Data such as Earth imagery undermines commitments to Convention on Biological Diversity (CBD) and Aichi targets. Artificial intelligence (AI) solutions to Big Data issues are urgently needed as these could prove to be faster, more accurate, and cheaper. Reducing costs of managing protected areas in remote deep waters and in the High Seas is of great importance, and this is a realm where autonomous technology will be transformative.